Abstract

The capability of different neural networks for predicting the main state variables (biomass, substrate and ethanol concentrations, the output variables) in Zymomonas mobilis CP4 batch fermentations has been tested. Experimental data recorded from batch fermentations carried out under different conditions (medium composition and temperature) were used to train the net and test its predictions. First a black-box neural network was designed for predicting the three output values given as input values of the present condition of the system. The configuration of the neural network that gives the best results is the one with 10 neurons in the hidden layer (total training error equal to 0.575 x 10-3, total testing error equal to 0.719 x 10-3). Better results can be obtained using different neural networks for each of the output variables (total testing error equal to 0.527 x 10-3). Attempts were made to enhance the prediction capability of neural network models, using the mathematical model that describes the process and a neural network for computing the kinetic parameters in the model. Here, the error is higher than in previous cases. The best prediction is obtained using a neural network with 30 neurons in the hidden layer (total training error equal to 5.535 x 10-3, total testing error equal to 2.225 x 10-3). The capability of different neural networks for predicting the main state variables (biomass, substrate and ethanol concentrations, the output variables) in Zymomonas mobilis CP4 batch fermentations has been tested. Experimental data recorded from batch fermentations carried out under different conditions (medium composition and temperature) were used to train the net and test its predictions. First a black-box neural network was designed for predicting the three output values given as input values of the present condition of the system. The configuration of the neural network that gives the best results is the one with 10 neurons in the hidden layer (total training error equal to 0.575×10-3 total testing error equal to 0.719×10-3). Better results can be obtained using different neural networks for each of the output variables (total testing error equal to 0.527×10-3). Attempts were made to enhance the prediction capability of neural network models, using the mathematical model that describes the process and a neural network for computing the kinetic parameters in the model. Here, the error is higher than in previous cases. The best prediction is obtained using a neural network with 30 neurons in the hidden layer (total training error equal to 5.535×10-3, total testing error equal to 2.225×10-3).